首页|CaSE: Explaining Text Classifications by Fusion of Local Surrogate Explanation Models with Contextual and Semantic Knowledge
CaSE: Explaining Text Classifications by Fusion of Local Surrogate Explanation Models with Contextual and Semantic Knowledge
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NSTL
Elsevier
Generating explanations within a local and model-agnostic explanation scenario for text classification is often accompanied by a local approximation task. In order to create a local neighborhood for a document, whose classification shall be explained, sampling techniques are used that most often treat the according features at least semantically independent from each other. Hence, contextual as well as semantic information is lost and therefore cannot be used to update a human's mental model within the according explanation task. In case of dependent features, such explanation techniques are prone to extrapolation to feature areas with low data density, therefore causing misleading interpretations. Additionally, the "the whole is greater than the sum of its parts" phenomenon is disregarded when using explanations that treat the according words independently from each other. In this paper, an architecture named CaSE is proposed that either uses Semantic Feature Arrangements or Semantic Interrogations to overcome these drawbacks. Combined with a modified version of Local interpretable model-agnostic explanations (LIME), a state of the art local explanation framework, it is capable of generating meaningful and coherent explanations. The approach utilizes contextual and semantic knowledge from unsupervised topic models in order to enable realistic and semantic sampling and based on that generate understandable explanations for any text classifier. The key concepts of CaSE that are deemed essential for providing humans with high quality explanations are derived from findings of psychology. In a nutshell, CaSE shall enable Semantic Alignment between humans and machines and thus further improve the basis for Interactive Machine Learning. An extensive experimental validation of CaSE is conducted, showing its effectiveness by generating reliable and meaningful explanations whose elements are made of contextually coherent words and therefore are suitable to update human mental models in an appropriate way. In the course of a quantitative analysis, the proposed architecture is evaluated w.r.t. a consistency property and to Local Fidelity of the resulting explanation models. According to that, CaSE generates more realistic explanation models leading to higher Local Fidelity compared to LIME.
NSTL
Elsevier
